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Characteristics and Drivers of High-Altitude Ladybird Flight: Insights from Vertical-Looking Entomological Radar

Daniel L. Jeffries1, Jason Chapman2,3, Helen E. Roy4, Stuart Humphries1, Richard Harrington2, Peter M. J. Brown5, Lori-J. Lawson Handley1* 1 Department of Biological Sciences, University of Hull, Hull, Humberside, United Kingdom, 2 Rothamsted Research, Harpenden, Hertfordshire, United Kingdom, 3 Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, United Kingdom, 4 NERC Centre for Ecology & Hydrology, Wallingford, Oxfordshire, United Kingdom, 5 Department of Life Sciences, Anglia Ruskin University, Cambridge, United Kingdom

Abstract Understanding the characteristics and drivers of dispersal is crucial for predicting population dynamics, particularly in range- shifting species. Studying long-distance dispersal in is challenging, but recent advances in entomological radar offer unique insights. We analysed 10 years of radar data collected at Rothamsted Research, U.K., to investigate characteristics (altitude, speed, seasonal and annual trends) and drivers (aphid abundance, air temperature, wind speed and rainfall) of high-altitude flight of the two most abundant U.K. ladybird species (native Coccinella septempunctata and invasive Harmonia axyridis). These species cannot be distinguished in the radar data since their reflectivity signals overlap, and they were therefore analysed together. However, their signals do not overlap with other, abundant insects so we are confident they constitute the overwhelming majority of the analysed data. The target species were detected up to ,1100 m above ground level, where displacement speeds of up to ,60 km/h were recorded, however most ladybirds were found between ,150 and 500 m, and had a mean displacement of 30 km/h. Average flight time was estimated, using tethered flight experiments, to be 36.5 minutes, but flights of up to two hours were observed. Ladybirds are therefore potentially able to travel 18 km in a ‘‘typical’’ high-altitude flight, but up to 120 km if flying at higher altitudes, indicating a high capacity for long-distance dispersal. There were strong seasonal trends in ladybird abundance, with peaks corresponding to the highest temperatures of mid-summer, and warm air temperature was the key driver of ladybird flight. Climatic warming may therefore increase the potential for long-distance dispersal in these species. Low aphid abundance was a second significant factor, highlighting the important role of aphid population dynamics in ladybird dispersal. This research illustrates the utility of radar for studying high-altitude flight and has important implications for predicting long-distance dispersal.

Citation: Jeffries DL, Chapman J, Roy HE, Humphries S, Harrington R, et al. (2013) Characteristics and Drivers of High-Altitude Ladybird Flight: Insights from Vertical-Looking Entomological Radar. PLoS ONE 8(12): e82278. doi:10.1371/journal.pone.0082278 Editor: Eric James Warrant, Lund University, Sweden Received June 25, 2013; Accepted October 21, 2013; Published December 18, 2013 Copyright: ß 2013 Jeffries et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was primarily funded by the Department of Biological Sciences, University of Hull. In addition, Rothamsted Research is a national instituteof bioscience strategically funded by the UK Biotechnology and Biological Sciences Research Council (BBSRC). HER is funded by the Natural Environment Research Council and also receives funding from the Joint Nature Conservation Committee. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected]

Introduction therefore increase the frequency and distance of migration, which, in the case of pest species, presents considerable challenges for 2 An estimated three billion insects fly through a 1 km ‘window’ conservation of native biodiversity and for sustainable agriculture. of sky in England during a typical summer month [1]. While a Ladybirds (Coleoptera: ) are the most abundant substantial proportion of these insects are beneficial and provide aphid predators in cereal crops worldwide, and are important essential ecosystem services, others are pests that pose a potential biological control agents of aphids and coccids [9]. However, use threat to biodiversity, the economy and human health. Knowledge of certain species in biological control has contributed to their of the characteristics (e.g. altitude and displacement speed, status as IAS in many parts of the world (see [6] and [10] for seasonal and annual trends) and drivers (e.g. prey abundance, recent reviews). For example, Coccinella septempunctata was intro- environmental factors) of insect flight is crucial for estimating long- duced to North America for biological control in the 1970s and distance dispersal capability, predicting the dynamics, persistence rapidly spread across the continent, gaining IAS status in the and spread of insect populations, and has important applications 1990s (reviewed in [1,10]). Soon afterwards (1988), another in pest management and conservation [2–6]. This knowledge is coccinellid, Harmonia axyridis, was found to be established and particularly important in the case of invasive alien species (IAS) quickly spreading in North America [6]. Since 1988, H. axyridis has and those undergoing range shifts in response to global warming established in at least 38 countries on four continents, with spread [1–7], since higher temperatures have been shown to drive rates estimated up to 500 km/year [6]. This rapid spread suggests increased migration in certain insects [8]. Global warming could considerable long-distance dispersal capability of this species, but

PLOS ONE | www.plosone.org 1 December 2013 | Volume 8 | Issue 12 | e82278 Characteristics and Drivers of Ladybird Flight estimates are confounded by accidental transport by humans [6]. Both H. axyridis and C. septempunctata are considered to be strong, Studying the dispersal capability of H. axyridis is particularly active fliers with high dispersal ability, at least over short distances important given that this species has been linked to declines in [16,18,19], but studying dispersal over longer distances has been indigenous ladybirds and is thought to present a threat to native hampered by the difficulty of tracking the insects in the field. biodiversity [11,12]. Conclusions have therefore been limited to assumptions inferred Knowledge of the key characteristics of ladybird dispersal; from ground level observations of walking [21,30,31] or short- specifically flight altitude, displacement speed, and seasonal and distance flights [21,24,27,32–34], or indirect evidence for long- annual patterns, is crucial to understanding and predicting long- distance flights (e.g. from malaise-traps, [35] or mark-release- distance dispersal. Since wind speed increases with altitude, species recapture experiments, [21,36]). To our knowledge, only one that use high-altitude wind currents have greater long-distance study has so far attempted to directly study coccinellids migrating dispersal potential than those that fly only a few metres above at high altitudes. Using traps on aircraft, Hagen [37] sampled ground level (AGL), where wind currents are negligible [13]. Hippodamia convergens flying to and from migration sites, between Flight altitude is therefore a key determinant of dispersal potential. approximately 1000 and 1650 metres above ground level. Our understanding of insect seasonal phenology tends to be drawn However only 24 were sampled in 15 aerial surveys. In from ground-level observations, but it is unclear whether this recent years, a new generation of vertical-looking entomological accurately reflects insect density and population dynamics. For radars (VLR) have made it possible to identify and record large instance, the number of individual records of H. axyridis from field insects flying at altitudes ,150–1200 m AGL, providing quanti- observations across the UK peaks in October, corresponding to tative estimates of insect aerial density, diversity and biomass, and the period when individuals are migrating to overwintering sites unique insights into insect population dynamics and the charac- [14], but whether this corresponds to a peak in actual abundance teristics and drivers of high-altitude [38–42] flight. Here, we is unclear. An important question is whether the potential for long- analyse VLR data collected at Rothamsted Research, Harpenden, distance dispersal is greater during the migration to or from U.K. between May-October 2000–2010, together with meteoro- overwintering sites or during the summer months, when meteo- logical and aphid suction trap data, and perform simple rological conditions may be more favourable for flight. experiments to investigate high-altitude flight in C. septempunctata Another key question is whether dispersal is driven mainly by and H. axyridis. Specifically, we investigate 1) flight characteristics biotic or environmental cues, or by a combination of both. This is in relation to altitude, duration and speed, and seasonal and likely to vary depending on the scale of dispersal, as there are annual trends, and 2) the potential role of biotic (aphid abundance) potentially different underlying causes for short and long-distance and environmental variables (temperature, wind speed and dispersal. Ladybird flight over short distances (classified as ,2m, rainfall) as drivers of high-altitude flight. We use time series and referred to as ‘‘trivial’’ or ‘‘appetitive flight’’ by [15]) is analyses to investigate trends in ladybird abundance in relation to year, season, aphid abundance and environmental variables, and considered to be for foraging or oviposition, whereas flight over perform a combination of linear modelling techniques to identify longer distances (.2 m), is thought to be in response to prey the main driver(s) of high-altitude flight. shortage or migration to or from over-wintering sites, which has an important physiological basis [15]. Several authors have reported a relationship between aphid density and ladybird emigration from Materials and Methods a local foraging patch, with emigration rates increasing with Vertical-looking radar (VLR) data and target species decreasing patch quality [9,16–23]. However, in some cases, even identification when aphids are abundant, a significant proportion of coccinellid Flight data for H. axyridis and C. septempunctata were obtained adults disperse [24,25], and the link between ladybird dispersal from vertical-looking entomological radar (VLR) data collected at and local aphid density often appears to be relatively weak [26]. Rothamsted Research in Harpenden (Hertfordshire), U.K. Radar Environmental variables such as ambient temperature, wind speed equipment, mode of operation, and analysis capabilities have been and rainfall, may be equally or even more important than prey previously described [1,43–46]. In brief, the radar detects insects density in triggering dispersal. Elliott [27] found that aphid passing through a nutating, vertical beam, within 15 altitude bands population density and ambient temperature were the main located between 150 and 1189 m AGL [1] (and see Supporting drivers of short-distance flights in three aphidophagous coccinel- Information Text S1). H. axyridis and C. septempunctata of both sexes lids, but that aphid density had no influence on longer flights. This were field-caught, weighed, and the two principal radar back- is surprising given that one of the motivations for flight over longer scattering terms were estimated from laboratory back-scattering distances is thought to be a response to food shortage [27]. measurements [47]. Selection criteria for filtering ladybird records Ambient temperature has a strong influence on insect metabolism from the VLR data were then based on expected body mass and and is considered an important predictor of insect flight [28]. back-scattering ratios for large ladybirds ([1,43,47] and Supporting Temperature is already known to act as a cue for migration to Information Text S1). Mass and shape are considered species over-wintering sites in some coccinellids, including H. axyridis [29], diagnostic characteristics [47], but species identification with the and accounted for most of the variation in emigration rates of VLR cannot be performed with complete confidence, as there Coccinella californica from experimental plots of alfalfa and oats [24]. may be additional species from which a minority of individuals fall It is therefore likely to play an important role in long-distance within the mass and shape range of the target species. H. axyridis dispersal, particularly if coccinellids are flying at high altitudes, and C. septempunctata have characteristically small body axis ratios where ambient temperature is much lower than at ground level. relative to other insects, including many other coccinellids Wind speed is thought to be either facilitative or inhibitory in (Chapman et al., unpublished data). Coccinellids such as Halyzia insect flight depending on its magnitude, and is potentially 16-guttata, Anatis ocellata, Harmonia 4-punctata, Myzia oblongoguttata, important in long-distance dispersal [6], although its impact on Coccinella magnifica and argus may overlap with the short-distance dispersal in coccinellids appears to be negligible mass and shape ranges used to identify our target species, however, [27]. Rainfall probably inhibits both short and long-distance these species are far less common than either C. septempunctata or H. dispersal, and to our knowledge this has not yet been investigated. axyridis. Indeed, based on a very large long term UK and

PLOS ONE | www.plosone.org 2 December 2013 | Volume 8 | Issue 12 | e82278 Characteristics and Drivers of Ladybird Flight dataset [14], the two target species represent 87.1% of the total aerial density, temperature, wind speed, rainfall and aphid records for the eight coccinellid species listed above (Table S1). abundance. Linear trends in the time series were then investigated Moreover, our extensive experience of capturing high-flying using linear regression, modified for time series data [52]. The insects in our aerial netting platform (at 200–250 m above the relationship between aerial density and the explanatory variables ground), gained from extended collection periods between 1999 was explored by cross-correlation plots of the auto-correlation and 2007 [7,41] indicate that the only abundant day-flying insects function (ACF) and partial ACF (See Supporting Information Text with ‘‘ladybird-like’’ body shape flying at these altitudes, are S1 for further details). indeed ladybirds. During this work, 91% of total ladybirds Second, the effects of aphid abundance, temperature, wind sampled were C. septempunctata. Based on this combined evidence, speed, and rainfall on ladybird aerial density were investigated we are confident that the overwhelming majority of data points more formally, using statistical modelling, in order to identify the correspond to the two target species, H. axyridis and C. main driver(s) of ladybird flight. We first used standard linear septempunctata. However, unfortunately, the large overlap between regression to investigate the relationship between aerial density the mass and shape of these target species means it is impossible to and each explanatory variable. Next, because of the violation of distinguish between them. The VLR data set was filtered to extract assumptions for linear regression, discussed below, we used records collected between 09:00 and 15:00 from May to October Generalised Linear Models (GLMs) and Generalised Least 2000–2010. The months between November and April were not Squares (GLS) models to identify the main driver(s) of high- analysed since records of ladybirds (and indeed most other insects) altitude flight. Full details of data exploration and model validation are negligible in the VLR data during this period. Aerial density are given in the Supporting Information Text S1. Briefly, we (AD), an estimate of the target insect abundance standardized by tested the assumptions of normality, homogeneity of variance, no the potential atmospheric sampling volume, was estimated for H. colinearity between explanatory variables, no overdispersion and axyridis and C. septempunctata from the VLR data using an no autocorrelation in the time series. Normality and homogeneity established protocol [1,43]. assumptions were violated for aerial density, aphid abundance and rainfall, and these variables were therefore log transformed for all Characteristics and drivers of high-altitude flight analyses. Evidence of colinearity was found between temperature Aerial density and displacement speed (i.e. speed relative to the and wind speed and between wind speed and rainfall (Figures S4 ground) of radar targets identified as large ladybirds were and S5 and Table S5), therefore wind speed was dropped from the estimated at each of the 15 altitude bands using the filtered full models (referred to hereon as the ‘‘partial model’’), and results VLR database. Potential duration and distance of flight were for full and partial models compared. No extreme outliers were investigated using a combination of displacement speed data detected in the data; however, plotting residuals and performing obtained from the VLR, and flight duration data from tethered standard Poisson regressions (i.e. a GLM with a Poisson flight experiments in a custom-built flight simulator. Full details of distribution and log link function) indicated that overdispersion the tethered flight experiments are provided in the Supporting might be a problem in the dataset. Quasi-Poisson GLMs Information Text S1, but briefly, 20 H. axyridis were tethered 3 (qpGLMs) were therefore performed to estimate and account for (individually) to the inside of a 1 m Perspex cube using fine fishing the dispersion parameter, r, in the models. Auto-correlation was line, and their flight activity video-recorded over a 2-hour period. investigated by examining the ACF value at different time lags in Video footage was then analysed to determine the mean and auto-correlation plots of the residuals obtained for selected models. maximum time spent in active flight during the 2-hour period. We detected marginally significant positive auto-correlation Monthly averages for each variable (aerial density, number of between the same month in different years and negative auto- records, temperature, wind speed, rainfall and aphid abundance) correlation at 15 and 27-month intervals (e.g. between May and were calculated for May to October 2000–2010. Temperature, August, or June and September of different years, Figure S6) and wind speed and rainfall data were obtained for Rothamsted from therefore examined its effect further using GLS models with and the U.K Met Office Unified Model [48]. Outputs were extracted without auto-correlation, as detailed below, to examine the impact for each hour of each day encompassing the period for which VLR of temporal auto-correlation on the results. data were filtered. Values at 15 altitudes corresponding to the 15 In both the qpGLM and GLS analyses, we compared full (i.e. VLR range gates were produced for each hour, providing an including aphid abundance, temperature, wind speed, and rainfall altitudinal and temporal profile for temperature and wind speed at as explanatory variables), and partial (excluding wind speed) the location of the Rothamsted Research VLR. Aphid abundance models to examine the effects of significant colinearity between data were obtained from the Rothamsted Insect Survey (RIS) wind speed and other environmental variables. The optimal Aphid Bulletin (http://www.rothamsted.ac.uk/insect-survey/ minimal model (i.e. containing only significant explanatory STAphidBulletin.php). Aphid data were collected in the same location as the VLR using a 12.2 m high suction trap, which variables) was found by dropping one explanatory variable in samples 0.75 m3 of air/s [49]. Suction trap data are reliable turn and each time applying an analysis of deviance (F) test. To proxies for estimates of aphid population size for the immediate examine the impact of temporal auto-correlation using GLS area around catch sites [50]. Since our target coccinellids are both modelling, we first performed model simplification, starting from generalist predators, we included records for all 21 aphid species both the full and partial models, which is equivalent to a standard published by the RIS Aphid Bulletin during the study period in the multiple linear regression. Next, we added date (month or year) as analysis (see Table S2 for species list). Although the 21 species an extra explanatory variable in the model, and tested for its recorded in the bulletin represent a small fraction of the total effects. Finally, we extended the full and partial models (excluding number of aphid species present in the UK, they are the date) to allow the residuals to have a temporal pattern, which numerically dominant species, and account for approximately relaxes the independence assumption [53]. We imposed an AR-1 87% of total aphid numbers (Table S2). auto-correlation structure (i.e. an autoregressive model of order 1), All statistical analyses were performed in R v2.15.1 [51]. First, which models the residuals and noise at time s as a function of the we performed time series and seasonal decomposition analyses residuals of time s – 1 [53]. GLS models were fitted using the [52] to investigate seasonal, annual and overall trends for ladybird Residual Maximum Likelihood (REML), and the retained models

PLOS ONE | www.plosone.org 3 December 2013 | Volume 8 | Issue 12 | e82278 Characteristics and Drivers of Ladybird Flight compared by their Akaike and Bayesian Information Criteria (AIC a negative relationship between temperature and wind speed. and BIC respectively) and log likelihood scores. Potential colinearity between explanatory variables was therefore investigated further during model validation (see below and Text Results S1) Is there evidence of auto-correlation or partial auto- Identification of target species and flight characteristics correlation in the time series? Significant auto-correlation in A total of 8935 large ladybird-type targets (i.e. presumed C. the same month of each year was found for ladybird aerial density, septempunctata and H. axyridis) were detected in the VLR data temperature and wind speed, but not for rainfall or aphid during the study period (see Data S1). There was an almost perfect abundance (Figure S3 a–e respectively). This was confirmed in the linear relationship between the number of records and aerial partial auto-correlations (Figure S3 f–j). Aerial density also exhibits 2 density (by year, R adj = 0.911, F1,9 = 103.4, P,0.001), therefore significant partial auto-correlation at a lag of five months. For we just present the results for aerial density here (see Table S3 for a example, it will be high in October, if it was high in May (Figure full breakdown of the sum and mean number of records and aerial S3 f). Aphids follow a similar pattern, but the partial ACF at a lag density by year and month). The highest aerial density was in 2006 of five months is not quite significant (Figure S3 j). (sum = 14317, monthly mean 2386, Standard Deviation, S.D. Is there evidence of cross-correlation between ladybird = 2969), with the lowest in 2000 (sum = 3014, monthly mean 502, aerial density and the explanatory variables? We focus S.D. = 587). Aerial density by month followed a normal here only on results from partial auto-correlations since they are distribution with a peak in August (sum = 26751, annual mean much more informative than auto-correlations for revealing = 2432, S.D. = 1924), and lowest values in October (sum = 788, relationships between variables as they account for correlation annual mean = 72, S.D. = 64) and May (sum = 2767, annual between successive points in the time series. All pair-wise cross mean = 252, S.D. 180, Figure 1, Figure S1, Table S3). correlations showed at least some significant peaks in the partial The target species were detected in gate numbers 1–14 of the auto-correlation plots, suggesting influence of the explanatory VLR (corresponding to altitudes between 150 and 1118 m AGL, variables on AD (Figure 3). However, patterns for temperature, Figure 1b and Table S4). Aerial density follows an exponential wind speed and aphids are particularly strong (Figure 3 a, b and d). distribution (Figure 1b), with the greatest number of detections in Partial auto-correlation is significant at each lag in the time series first range gate (150–195 m AGL, AD sum = 18145, mean = 13, for AD versus temperature (Figure 3a) and wind speed (Figure 3b), S.D. = 7) and the fewest in the fourteenth range gate and for the majority of lags in the time series of AD versus aphids (corresponding to 1073–1118 m AGL, aerial density sum = 4.0, (Fig 3e). This indicates that temperature, wind speed and aphid mean = 2.0, S.D. = 0.1). The majority (85.2%) of ladybirds were abundance are key drivers of ladybird aerial density. However, as detected in the first five range gates, indicating that most ladybird mentioned above, wind speed and temperature are not indepen- flight within the detected range occurred roughly between ,150 dent. Indeed, the relationship between AD and wind speed shows and 500 m AGL (Table S4). the exact opposite trend to that for AD and temperature (i.e. a Mean displacement speed of the target species ranged from significant positive peak for aerial density versus temperature 8.5 m/s (S.D. = 4.2) for gate 1 to 16.4 m/s (S.D. = 1.3) for gate corresponds to a significant negative peak for aerial density versus 14 (Table S4). The mean displacement speed of the target species wind speed, or vice versa), which indicates strong co-linearity in the first 5 gates (corresponding to 85% of the detections, as between wind speed and temperature. noted above) was 8.4 m/s (S.D. = 0.3). Tethered flight experi- ments for H. axyridis produced a mean uninterrupted flight Statistical models duration of 36.5 (635.5) minutes, with a maximum flight duration What are the key driver(s) of high-altitude flight? Signi- of 2 hours, at which time flights were stopped. ficant relationships were identified between AD and both temperature and aphid abundance in the standard linear Time series analyses regressions (Figure 4 a and d). Aerial density increases with Are there any seasonal, annual or overall trends in the increasing temperature up to approximately 19uC (Figure 4 a), time series? Both basic time series plots (Figure 2) and seasonal with temperature explaining approximately 19% of the variance 2 decomposition plots (Figure S2) demonstrate clear seasonal peaks in AD (adjusted R squared value, R adj = 0.186, F1,58 = 14.47, and troughs for ladybird aerial density and aphid abundance. P = 0.000). There is a weak, but significant, negative relationship Ladybird aerial density peaked during midsummer of 2005, 2006 between aphid abundance and AD (Figure 4d), with aphids 2 and 2010, suggesting a positive relationship with temperature, explaining approximately 6% of the variance (R adj = 0.056, whereas aphid numbers troughed in midsummer, and peaked late F1,58 = 4.527, P = 0.038). No relationship was found between 2 summer/early autumn of 2004 and 2010, and then in the spring of AD and either rainfall (R adj = 20.011, F1,58 = 0.358, P = 0.552, 2 the following years. Peaks in aphid abundance therefore precede Figure 4c) or wind speed (R adj = 0.034, F1,58 = 3.080, those for ladybird aerial density, and ladybird flight activity is at its P = 0.085, Figure 4b). The importance of both temperature highest when aphid numbers are at their lowest. However this and aphid abundance as predictors of aerial density were association is complex. For example, high aphid abundance does confirmed by our GLM and GLS analyses, as discussed below. not explain the peak in ladybird aerial density for 2006, since 2005 We found a strong, negative linear relationship between was a poor year for aphids. temperature and wind speed (correlation coefficient = 20.8, 2 212 Plots suggest linear trends for temperature and wind speed over R adj = 0.574, F1,58 = 80.470, P = 1.484610 ) and a positive the whole study period, and this was confirmed by linear linear relationship between wind speed and rainfall (correlation 2 regressions, which showed a significant increase in temperature coefficient = 0.3, R adj = 0.101, F1,58 = 7.654, P = 0.008, see 2 (R adj = 0.287, F1,58 = 24.77, P = 0.000) and decrease in wind Table S5). Partial models, excluding wind speed were therefore 2 speed (R adj = 0.175, F1,58 = 13.49, P = 0.001) over the time series. constructed and compared to full models to examine the effect of Time series plots do not however indicate any linear trends for colinearity between wind speed and other environmental vari- aerial density, rain or aphids. The time series plots also indicate ables. The effects of removing wind speed from the full qpGLM potential relationships between explanatory variables, particularly and GLS models are presented in full in Text S1. Briefly,

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Figure 1. Aerial density summarized by a) month and b) altitude for 2000–2010. Figure 1a Box plot for ladybird aerial density summarized by month. Boxes correspond to the 25th and 75th percentile, horizontal bars within boxes to means, and whiskers to maximum values or 1.5 times the interquartile range (when there are outliers present, represented by open circles). For boxplots of aerial density by year, or number of target species records in the VLR database, see Figure S1. Figure 1b Barplot of percentage total aerial density by altitude. The majority (roughly 85%) of ladybirds were detected in the first 5 range gates. Gate numbers correspond to the following altitudes (AGL): 1: 150–195; 2: 221–266; 3: 292–337; 4: 363–408; 5: 434–479; 6: 505–550; 7: 576–621; 8: 647–692; 9: 718–763; 10: 789–834; 11: 860–905; 12: 931–976; 13: 1002–1047; 14: 1073–1118; 15: 1144–1189. doi:10.1371/journal.pone.0082278.g001 removing wind speed from the full models increased the level of log likelihood scores in the partial compared to full models (Tables significance for temperature in both analyses, but did not affect S6 and S7). other explanatory variables. Removing wind speed also improved Temperature and aphid abundance were the only significant the GLS models slightly, as demonstrated by lower AIC, BIC and predictors of ladybird aerial density in the full and partial qpGLMs

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Figure 2. Time series plots for each variable. Results of time series analysis for aerial density and each explanatory variable for May to October 2000–2010. Note the correspondence between peaks in temperature and aerial density, and the lag between peaks in aerial density and aphid abundance. doi:10.1371/journal.pone.0082278.g002

Figure 3. Partial auto-correlation plots for aerial density against the four explanatory variables. ‘‘ACF’’ is the auto-correlation function, and ‘‘AD’’ Aerial density. Peaks that cross the dotted blue lines are considered significant at the 5% level. All explanatory variables show at least some significant peaks suggesting some influence on aerial density, however patterns for temperature, wind speed and aphids are particularly strong (Figure 3 a, b and d). doi:10.1371/journal.pone.0082278.g003

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Figure 4. Linear regression of aerial density against all explanatory variables. Graphs show the relationship between monthly mean aerial density and each of the explanatory variables (also monthly means). Units for the explanatory variables are: temperature: uC, wind speed: m/s, rainfall: mm, aphids: absolute number counted in suction trap. Note aerial density, rainfall and number of aphids are not normally distributed and are 2 2 therefore log transformed (see main text). ‘‘Rsq’’ = adjusted R (R adj). Temperature and aphids are both significant predictors of aerial density (see main text). doi:10.1371/journal.pone.0082278.g004

Table 1. Predictors of aerial density: Minimal (optimal) model.

Variable qpGLM t (P) qpGLM deviance (F and P ) GLS without auto-correlation t(P) GLS with auto-correlation t(P)

Aphid abundance 22.292 (0.026+) 23.750 (F = 4.982, P =0.030+) 22.273 (0.027+) 22.349 (0.022+) Temperature 3.847 (0.000***) 26.046 (F = 10.782, P = 0.002*) 3.879 (0.000***) 3.601 (0.001**) AIC n/a n/a 185.729 184.958 BIC n/a n/a 193.901 195.173 Log likelihood n/a n/a 288.865 287.479

Results for minimal qpGLMs and GLS models (See Tables S6 and S7 for results of full model including wind speed and partial model excluding wind speed, respectively). For the qpGLMs, both t statistic, and F statistic with corresponding and P values are given. The qpGLM dispersion parameter, r = 0.374, and residual deviance = 22.932 on 57 degrees of freedom (df). Residual df = 57 for each model. Note that the GLM deviance, F and P values correspond to the full model, where residual deviance is 21.777 on 55 df. GLS without autocorrelation is equivalent to standard multiple linear regression. P value codes: ***P,0.000; **P,0.001; *P,0.01; +P,0.05. doi:10.1371/journal.pone.0082278.t001

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(Tables S6 and S7), and were therefore retained in the minimal accurately reflect flight in the field, it suggests that on average (i.e. (optimal) model (Table 1). Dropping temperature or aphid assuming speeds of 30 km/h for 36.5 minutes and that meteoro- abundance from the models also resulted in a significant reduction logical conditions are similar to those in this study), ladybirds can in the explained deviance (Table 1). Although co-linearity between fly 18 km in a single flight. However, a few individuals, flying at wind speed and other environmental variables was a potential very high altitudes and for longer periods (assuming 59 km/h for concern, we found no evidence of an interaction between two hours), could potentially disperse almost 120 km in a single temperature and wind speed (t = 21.563, P = 0.125) or between flight. Little similar data exists from other taxa for comparison, rainfall and wind speed (t = 21.955, P = 0.057) in the full however moths and butterflies, also in the south of England, have qpGLMs, suggesting wind speed has little influence on the been estimated to travel up to 400–700 km in a single 8 hour outcome. migratory flight [48], which is broadly in line with our estimates. Temperature and aphid abundance were also the only Time series analyses demonstrated clear seasonal cycles in significant predictors of aerial density in full and partial GLS ladybird flight, with peak aerial density corresponding to models (Tables S6 and S7) and were therefore retained in the midsummer, and lowest aerial densities in May and October. minimal model (Table 1). Date (month or year) was not a Low aerial density in May corresponds to the period when C. significant predictor in the full or partial GLS models when added septempunctata and H. axyridis adults are typically mating, supporting as an extra explanatory variable, and adding date increased the a previous study which demonstrated that the flight activity of C. AIC, BIC and log likelihood scores. Adding an auto-correlation septempunctata drops to near zero when breeding [60]. C. structure did not improve the full or partial GLS models, and did septempunctata and H. axyridis begin to fly to overwintering sites not change overall conclusions about the importance of explan- during October [9], and it may therefore seem surprising that no atory variables. Adding an auto-correlation structure to the peak in aerial density was found during this month. In the U.K. minimal GLS model slightly reduced the significance of temper- however, overwintering sites are generally close to breeding sites, ature, but did not change the outcome for aphid abundance, nor so high-altitude flight is not likely to be required. Moreover, improve the minimal model (Table 1). generally cool temperatures and higher wind speeds at this time of Together, the linear regression, qpGLM and GLS results year could limit opportunities for high-altitude flights, as was demonstrate that both temperature and aphid abundance are found in C. septempunctata in Northern [56]. It is, significant drivers of high-altitude ladybird flight, with temperature therefore, likely that flights to overwintering sites occur below being the most important explanatory variable. The results are 150 m AGL (the minimum detection threshold of the VLR), robust to different methods of analysis, and are essentially which matches increases in observations of C. septempuncata at 12.5 unaffected by confounding factors such as co-linearity and auto- – 27 m AGL during migrations to diapause sites in Hungary [56]. correlation. Similar seasonal patterns to those found here have been found for C. septempunctata from U.K. Ladybird Survey records (phenogram Discussion based on 27,000 records collected over 1785610 km squares between 1990 and 2010 [14]). However the U.K. Ladybird Survey Characteristics of high-altitude ladybird flight phenogram for H. axyridis (based on 25,676 records collated from Previous studies have been limited to studying coccinellids flying 1099610 km squares between 2004 and 2010 [14]), provides a at or near ground level (e.g. [21,24,27,30,31,33,52–55]. In this different picture, with a small peak in the number of records study we aimed to enhance understanding of high-altitude flight between June and July, and greatest numbers between October for two economically and ecologically important coccinellids, both and November. This difference can be explained by the preference of which are considered IAS in large parts of the world. Data for H. axyridis, but not C. septempunctata, to overwinter inside obtained from vertical-looking radar shows that ladybirds have a buildings in very large numbers, making them highly conspicuous high propensity for high-altitude flight, with flight recorded up to during autumn. 1118 m above ground level. However, the majority (85%) of ladybirds were found between 150 m (the lowest range gate of the Drivers of high-altitude ladybird flight VLR) and 479 m, perhaps due to decreasing air temperatures and A complex but well-established link exists between aphidopha- energetic requirements of reaching higher altitudes. gous coccinellids and the population dynamics of their prey Direct estimates of the long-distance dispersal capability of [26,61–63] and the density of adult ladybirds is often positively coccinellids are crucial for accurately predicting spread beyond correlated with aphid density [9. 20, 64, 65]. Previous work has native ranges, and could inform risk assessment. Indirect estimates shown that ladybird emigration rate often decreases with of the spread of H. axyridis in its invasive range vary from 105 km/ increasing prey abundance ([19,21,22,24,27] and see [26] for year in the U.K. [57] to 500 km/year in South Africa [58], but review), but this pattern has not been confirmed for all these estimates are influenced by anthropogenic factors and/or aphidophagous coccinellids studied, including H. axyridis [22], obtained from historical records, which may be incomplete [6]. and so far studies have focused on dispersal for foraging over short Here we estimated the speed and potential distance of ladybirds distances (but see [21]). We therefore aimed to answer the flying at high altitudes, using a combination of data from tethered question: ‘‘can changes in aphid abundance explain high-altitude flight experiments and the VLR. The mean displacement speed of flight and long-distance dispersal in C. septempunctata and H. axyridis, ladybirds detected flying through the VLR sampling volume or are environmental variables, most notably temperature, more ranged from 31 km/h at 150 m AGL to 59 km/h at 1500 m important?’’ Time series analyses highlighted the association AGL, with the majority of ladybirds (85%, detected at altitudes of between ladybirds and their aphid prey, with peaks in aphid 150 – 479 m AGL) having a mean velocity of 30 km/h. Mean abundance (generally seen in autumn and spring) preceding those flight time for H. axyridis was 36.5 minutes in tethered flight for ladybird aerial density (in midsummer), consistent with experiments, which is similar to that found by Rankin & Rankin ladybirds dispersing at times of low aphid abundance. This was [59] in Hippodamia convergens, although some individuals continued supported by a weak but significant, negative relationship between flying for the two-hour duration of the experiment. Although it is aphid suction trap catches and ladybird aerial density, with aphids impossible to say whether experimental estimates of flight time explaining approximately 6% of the variation in aerial density.

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Aphid abundance (together with temperature, discussed below) increase, such barriers to dispersal may be compromised and this was retained in all three of our statistical models, indicating could potentially facilitate the spread of these species. Predictive significant association with peaks in ladybird flight. Aphid models of range expansion in invasive species are needed to inform abundance therefore appears to be an important driver of ladybird response strategies [67], and accuracy of these models will increase dispersal. It should be noted however that only dispersing aphids as the number of assumptions about an invader’s biology decreases are caught in the suction traps, but there is good evidence that [68]. Insights into the characteristics and drivers of dispersal, as suction trap catches are indicative of aphid numbers in the field produced here, are therefore crucial if comprehensive predictive [2,50]. models are to be used for risk assessment and management of IAS. Although our results indicate that aphids clearly influence dispersal, they demonstrate that ambient temperature is a much Supporting Information more important driver of ladybird flight in the U.K. Time series analyses suggested a positive relationship between monthly Figure S1 Boxplots of the number of target species VLR temperature and ladybird aerial density, with peak measurements records and aerial density by Month (a and b) and Year corresponding to high summer. This was confirmed with linear (c and d). regressions, in which temperature (between 5 and 19uC) explained (TIFF) approximately 19% of the variation in aerial density. Moreover, Figure S2 Decomposition of a) aerial density and b) temperature was retained, and highly significant in all of our aphid abundance time series into seasonal, trend and statistical models. This is perhaps not surprising given that remainder components. The seasonal component was esti- temperature has long been implicated as the single most important mated by taking the average per month. predictor of insect flight [28]. In addition, temperature has (TIFF) previously been shown to influence emigration rate from experimental alfalfa and oat fields in Coccinella californica [24] and Figure S3 Auto-correlation (a–e) and partial auto- ground level dispersal over short (,2 m) or longer (.2m) correlation (f–j) plots. ‘‘ACF’’ is the auto-correlation function. distances in Coleomegilla maculata, Hippodamia convergens, and Peaks that cross the dotted blue lines are considered significant at Hippodamia tredecimpunctata [27]. However its impact on high- the 5% level. altitude flight has been rarely investigated in any insect (but see (TIFF) [43,45,66]) and its effect is of critical importance given the Figure S4 Pairplot of aerial density and explanatory increasing pressure from global warming, and the implications for variables. The lower diagonal panels contain the absolute increased long-distance dispersal ability of range-shifting species. correlations, with the font size proportional to the value. The We predicted that wind speed and rainfall would also influence upper diagonal shows the pair-wise scatter plots with LOESS high-altitude ladybird flight, since wind speed is thought to be smoothing lines added. Note aerial density, rainfall and aphids are either facilitative or inhibitory in insect flight depending on its log transformed. magnitude [6], and rainfall could be expected to have an (TIFF) inhibitory effect on flight. For example, [40] found that the nocturnal moth Autographa gamma actively chooses high-altitude Figure S5 Relationship between explanatory variables. wind jets and also compensates for cross-wind drift to facilitate Pair-wise linear regression between explanatory variables. Note 2 long distance dispersal. However we found no effect of either rainfall and aphids are log transformed. ‘‘Rsq’’ = R adj (adjusted variable, even after accounting for co-linearity between wind speed R2). and temperature, and after removing temperature from statistical (TIFF) models. Note though, that our analysis was restricted to ground- Figure S6 Auto-correlation plot of residuals for the level wind speed data, recorded by the U.K. Met Office. Although selected minimal model (which includes the explanatory ground-level wind speed is a good proxy for wind speed at higher variables temperature and aphid abundance). ‘‘ACF’’ is the auto- altitudes, the potential role of windborne dispersal at high altitudes correlation function. Peaks that cross the dotted blue lines are should be investigated further in ladybirds. considered significant at the 5% level. The plot demonstrates In the current study we were only able to consider environ- marginally significant positive auto-correlation between the same mental variables and aphid abundance as potential drivers of month in different years, and negative auto-correlation at a lag of dispersal. Other potential drivers include the physiological state of 9 and 15, which corresponds to 15 and 27-month intervals in real- beetles at the onset and completion of diapause. Further study is time (e.g. between May and August, or June and September of needed to determine the relative importance of physiological compared to other biotic and abiotic cues. different years). (TIFF) Conclusions and implications for long-distance dispersal Table S1 Length ranges and number of records for all In conclusion, C. septempunctata and H. axyridis, have a high large ladybird species in the U.K. ladybird survey propensity for long-distance dispersal, having been detected flying between 1990 – 2010. over southern England at altitudes up to 1118 m AGL at speeds of (DOCX) up to 59 km/h. Temperature is a key driver of high-altitude Table S2 The proportion of the total number of aphid ladybird flight at least in the U.K., and low aphid abundance is catches for all species detected by the Rothamsted likely an important cue for long distance dispersal. Temperature Insect Survey (RIS) UK suction trap network that is may in fact partly explain the colonisation pattern of H. axyridis in accounted for by the 21 aphid species included in the RIS the U.K. which, despite a rapid spread across southern England, aphid bulletin. has slowed dramatically since it reached the boundaries of the (DOCX) Cambrian and Pennine mountains [6]. It may be that colder temperatures in these mountains inhibit flight and therefore act as Table S3 Summary of the number of target species VLR barriers to dispersal in the U.K. If global temperatures continue to records and aerial density by year and month. The total

PLOS ONE | www.plosone.org 10 December 2013 | Volume 8 | Issue 12 | e82278 Characteristics and Drivers of Ladybird Flight number of target species records in the VLR database is 8935. Table S7 Drivers of high-altitude flight: partial model ‘‘S.D.’’ Standard deviation. (excluding wind speed). Dispersion parameter for qpGLM (DOCX) r = 0.367, and deviance 22.123 on 56 df. P value codes: *** , ** , * , + , Table S4 Summary of the number of target species VLR P 0.000; P 0.001; P 0.01; P 0.05. records, aerial density and displacement speed by (DOCX) altitude. Text S1 Supplementary Methods and Results. (DOCX) (DOCX) Table S5 Relationship between explanatory variables. Data S1 Raw data file. Final VLR data set filtered for C. Results of pair-wise linear regression exploring collinearity septempunctata and H. axyridis like hits. For an explanation of sigma 2 between explanatory variables. Upper diagonal = R adj (adjusted values please see Supplementary Text S1. 2 R ), lower diagonal = F statistic and P value (in brackets). All F (XLSX) statistics are on 1 and 58 degrees of freedom. Significant relationships are highlighted in bold. Note rainfall & aphids are Acknowledgments both transformed. (DOCX) We are grateful to Alan Smith and Jason Lim for technical assistance with the VLR calibration, and to two referees for constructive feedback on our Table S6 Drivers of high-altitude flight: full model manuscript. (including wind speed). Results shown for qpGLMs and GLS are for full model containing all explanatory variables Author Contributions including wind speed. ‘‘qpGLM is quasi-Poisson full model. Both t statistic, and F statistic with corresponding and P values given. Conceived and designed the experiments: LLH JC. Performed the Dispersion parameter for qpGLM r = 0.369, and deviance 21.777 experiments: DLJ. Analyzed the data: DLJ LLH. Contributed reagents/ on 55 df. GLS without autocorrelation is equivalent to standard materials/analysis tools: JC RH. Wrote the paper: LLH DLJ. Provided advice on analyses: SH. Contributed to manuscript: HER PMJB. multiple linear regression. P value codes: ***P,0.000; **P,0.001; Performed additional analyses for the revised manuscript: PMJB. *P,0.01; +P,0.05. (DOCX)

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